@inproceedings{zhang-etal-2018-attention,
title = "Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction",
author = "Zhang, Ningyu and
Deng, Shumin and
Sun, Zhanling and
Chen, Xi and
Zhang, Wei and
Chen, Huajun",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1120",
doi = "10.18653/v1/D18-1120",
pages = "986--992",
abstract = "A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.",
}
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<abstract>A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.</abstract>
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%0 Conference Proceedings
%T Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
%A Zhang, Ningyu
%A Deng, Shumin
%A Sun, Zhanling
%A Chen, Xi
%A Zhang, Wei
%A Chen, Huajun
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhang-etal-2018-attention
%X A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.
%R 10.18653/v1/D18-1120
%U https://aclanthology.org/D18-1120
%U https://doi.org/10.18653/v1/D18-1120
%P 986-992
Markdown (Informal)
[Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction](https://aclanthology.org/D18-1120) (Zhang et al., EMNLP 2018)
ACL